Volumetric assessment of glioblastoma and its predictive value for survival

Henker, Christian; Hiepel, Marie Cristin; Kriesen, Thomas; Scherer, Moritz; Glass, Änne; Herold‐Mende, Christel; Bendszus, Martin; Langner, Sönke; Weber, Marc‐André; Schneider, Björn; Unterberg, Andreas; Piek, Jürgen

doi:10.1007/s00701-019-03966-6

Cited by 20 publications

(27 citation statements)

References 33 publications

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“…Present results confirm that, the higher the volume of the FLAIR-T 2 image, the worse the prognosis is. However, there is some controversy in this respect, since some studies have shown correlations between FLAIR-T 2 hyper-intensities with longer OS ( 13 – 18 , 40 , 47 ), while others have not been able to prove it ( 12 , 13 , 48 , 49 ). Many factors, including the non-exclusion of cystic cavities, the influence of corticosteroid treatment, and the inclusion of both, IDH-mutated, and IDH wild-type tumors, may further underlie these discrepancies.…”

Section: Discussionmentioning

confidence: 99%

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Assessment of Pre-operative Measurements of Tumor Size by MRI Methods as Survival Predictors in Wild Type IDH Glioblastoma

et al. 2020

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Objective: We evaluate the performance of three MRI methods to determine non-invasively tumor size, as overall survival (OS) and Progression Free Survival (PFS) predictors, in a cohort of wild type, IDH negative, glioblastoma patients. Investigated protocols included bidimensional (2D) diameter measurements, and three-dimensional (3D) estimations by the ellipsoid or semi-automatic segmentation methods. Methods: We investigated OS in a cohort of 44 patients diagnosed with wild type IDH glioblastoma (58.2 ± 11.4 years, 1.9/1 male/female) treated with neurosurgical resection followed by adjuvant chemo and radiotherapy. Pre-operative MRI images were evaluated to determine tumor mass area and volume, gadolinium enhancement volume, necrosis volume, and FLAIR-T 2 hyper-intensity area and volume. We implemented then multivariate Cox statistical analysis to select optimal predictors for OS and PFS. Results: Median OS was 16 months (1–42 months), ranging from 9 ± 2.4 months in patients over 65 years, to 18 ± 1.6 months in younger ones. Patients with tumors carrying O 6 -methylguanin-DNA-methyltransferase (MGMT) methylation survived 30 ± 5.2 vs. 13 ± 2.5 months in non-methylated. Our study evidenced high and positive correlations among the results of the three methods to determine tumor size. FLAIR-T 2 hyper-intensity areas (2D) and volumes (3D) were also similar as determined by the three methods. Cox proportional hazards analysis with the 2D and 3D methods indicated that OS was associated to age ≥ 65 years (HR 2.70, 2.94, and 3.16), MGMT methylation (HR 2.98, 3.07, and 2.90), and FLAIR-T 2 ≥ 2,000 mm 2 or ≥60 cm 3 (HR 4.16, 3.93, and 3.72), respectively. Other variables including necrosis, tumor mass, necrosis/tumor ratio, and FLAIR/tumor ratio were not significantly correlated with OS. Conclusion: Our results reveal a high correlation among measurements of tumor size performed with the three methods. Pre-operative FLAIR-T 2 hyperintensity area and volumes provided, independently of the measurement method, the optimal neuroimaging features predicting OS in primary glioblastoma patients, followed by age ≥ 65 years and MGMT methylation.

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Section: Discussionmentioning

confidence: 99%

“…Additionally, some studies have reported associations with survival of the necrosis volume, enhancement tumor volume, and the tumor/necrosis ratio ( 13 , 18 , 48 ). Our study could not confirm these, either in either bivariate or multivariate analyses.…”

Section: Discussionmentioning

confidence: 99%

Assessment of Pre-operative Measurements of Tumor Size by MRI Methods as Survival Predictors in Wild Type IDH Glioblastoma

et al. 2020

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“…The association between FLAIR proportion relative to other subregions and survival is unclear [38]. PTE/TC was not found to be associated with OS in a previous study [34]. Multiparametric biopsies have shown high levels of viable tumour cells within the non-enhancing region [39].…”

Section: Discussionmentioning

confidence: 94%

“…Instead, higher relative proportion of necrosis is associated with worse prognosis. NC/TC has been previously associated with worse survival [30,34]. However, this study only included patients suitable for CRET, raising the possibility of selection bias.…”

Section: Discussionmentioning

confidence: 98%

“…However, this study only included patients suitable for CRET, raising the possibility of selection bias. Furthermore, they did not control for qualitative imaging features described in the VASARI feature set in their multivariable analysis [34]. Hypoxia may select for quiescent stem-like cells around the NC which resist apoptosis and undergo proliferation [35].…”

Section: Discussionmentioning

confidence: 99%

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Deep learning for glioblastoma segmentation using preoperative magnetic resonance imaging identifies volumetric features associated with survival

2020

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Background Measurement of volumetric features is challenging in glioblastoma. We investigate whether volumetric features derived from preoperative MRI using a convolutional neural network–assisted segmentation is correlated with survival. Methods Preoperative MRI of 120 patients were scored using Visually Accessible Rembrandt Images (VASARI) features. We trained and tested a multilayer, multi-scale convolutional neural network on multimodal brain tumour segmentation challenge (BRATS) data, prior to testing on our dataset. The automated labels were manually edited to generate ground truth segmentations. Network performance for our data and BRATS data was compared. Multivariable Cox regression analysis corrected for multiple testing using the false discovery rate was performed to correlate clinical and imaging variables with overall survival. Results Median Dice coefficients in our sample were (1) whole tumour 0.94 (IQR, 0.82–0.98) compared to 0.91 (IQR, 0.83–0.94 p = 0.012), (2) FLAIR region 0.84 (IQR, 0.63–0.95) compared to 0.81 (IQR, 0.69–0.8 p = 0.170), (3) contrast-enhancing region 0.91 (IQR, 0.74–0.98) compared to 0.83 (IQR, 0.78–0.89 p = 0.003) and (4) necrosis region were 0.82 (IQR, 0.47–0.97) compared to 0.67 (IQR, 0.42–0.81 p = 0.005). Contrast-enhancing region/tumour core ratio (HR 4.73 [95% CI, 1.67–13.40], corrected p = 0.017) and necrotic core/tumour core ratio (HR 8.13 [95% CI, 2.06–32.12], corrected p = 0.011) were independently associated with overall survival. Conclusion Semi-automated segmentation of glioblastoma using a convolutional neural network trained on independent data is robust when applied to routine clinical data. The segmented volumes have prognostic significance.

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State-of-the-art imaging for glioma surgery

Verburg

Hamer

2020

Neurosurg Rev

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Diffuse gliomas are infiltrative primary brain tumors with a poor prognosis despite multimodal treatment. Maximum safe resection is recommended whenever feasible. The extent of resection (EOR) is positively correlated with survival. Identification of glioma tissue during surgery is difficult due to its diffuse nature. Therefore, glioma resection is imaging-guided, making the choice for imaging technique an important aspect of glioma surgery. The current standard for resection guidance in non-enhancing gliomas is T2 weighted or T2w-fluid attenuation inversion recovery magnetic resonance imaging (MRI), and in enhancing gliomas T1-weighted MRI with a gadolinium-based contrast agent. Other MRI sequences, like magnetic resonance spectroscopy, imaging modalities, such as positron emission tomography, as well as intraoperative imaging techniques, including the use of fluorescence, are also available for the guidance of glioma resection. The neurosurgeon’s goal is to find the balance between maximizing the EOR and preserving brain functions since surgery-induced neurological deficits result in lower quality of life and shortened survival. This requires localization of important brain functions and white matter tracts to aid the pre-operative planning and surgical decision-making. Visualization of brain functions and white matter tracts is possible with functional MRI, diffusion tensor imaging, magnetoencephalography, and navigated transcranial magnetic stimulation. In this review, we discuss the current available imaging techniques for the guidance of glioma resection and the localization of brain functions and white matter tracts.

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Volumetric assessment of glioblastoma and its predictive value for survival

Cited by 20 publications

References 33 publications

Assessment of Pre-operative Measurements of Tumor Size by MRI Methods as Survival Predictors in Wild Type IDH Glioblastoma

Assessment of Pre-operative Measurements of Tumor Size by MRI Methods as Survival Predictors in Wild Type IDH Glioblastoma

Deep learning for glioblastoma segmentation using preoperative magnetic resonance imaging identifies volumetric features associated with survival

State-of-the-art imaging for glioma surgery

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